In the past, several unsupervised methods have been proposed that are able to detect anomalies in multivariate time-series data [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Among these, the traditional models include ensemble based Isolation Forest (IF) [3]}, memory efficient local outlier detection (DILOF) [4]} and an extended Kalman filter based one-class SVM anomaly detector (OCSVM) [5]}. Such models are known to be quick in anomaly detection, but can incur low accuracy for multi-dimensional and complex data [6]}. We compare GON against IF, DILOF and OCSVM as baselines.
